The non-destructive investigation of objects by transmission or emission tomography imaging methods is generally known. Applications of tomography imaging, like e.g. CT imaging and PET imaging have been developed not only for medical examinations, but also in other technical fields, like e.g. materials sciences or constructions techniques.
CT imaging is based on an X-ray irradiation through a sample plane of the region of investigation with different projection directions. The collected projection data, which comprise attenuation data are subjected to a reconstructing procedure for obtaining an image function representing an image of the region of investigation.
PET imaging is based on the detection of gamma rays emitted by a positron emitting tracer substance after a positron annihilation event. The gamma radiation is detected along a plurality of projection directions (line of response direction, or coincidence line direction). Again, for obtaining the tomographic image, the projection data are subjected to a reconstructing procedure. The entirety of measured projection data, like the attenuation data of CT imaging or the coincidence data of PET imaging, represent so-called Radon data in a Radon space.
Reconstructing an image function on the basis of Radon data can be implemented with the filtered back-projection (FBP), iterative reconstruction methods or polynomial-based algorithms (OPED algorithm, US 2008 0130974 A1). The detection and the reconstructing methods may cause artifacts in the image function, like e.g. streak artifacts. Those artifacts can occur in images of objects including large contrast gradients, e.g. at sharp edges, or as a result of detector noise (noise induced artifacts). Artifacts in transmission or emission tomography may represent an essential restriction of the application of the imaging method.
As an example, artifacts may result in a wrong diagnosis or even a wrong therapeutic irradiation treatment, which is planned on the basis of CT images. In particular, due to artifact generation, the reconstruction algorithms OPED and FBP cannot be used for applications of PET imaging in nuclear medicine. Typically, PET images are reconstructed with the iterative reconstructing methods, which however have a drawback in terms of time consumption.
Artifact generation in conventional reconstructing methods represents in particular a disadvantage if different imaging techniques are to be combined. Combination of CT and PET imaging suffers from the different processing times as a result of the relatively fast OPED and FBP algorithms used for CT imaging and the slow iterative methods used for PET imaging.
Conventionally, attempts have been made for reducing imaging artifacts on the basis of physical approaches, e.g. by optimizing the scanning system or the scanning procedure. Optimization has been made for adapting the measured projection data to the requirements of both the scanning conditions and the reconstruction algorithms. As the aliasing artifacts are created as a result of an insufficient scanning resolution near the steep contrast gradient, a so-called detector offset technique has been proposed for doubling the sampling density. A further increase of the sampling density can be obtained with the “flying focus”-procedure, wherein an additional degree of freedom of the focus of the beam source of a CT device is used. These conventional techniques have disadvantages in terms of increased processing costs due to the essentially increased amount of data and complexity of the imaging device.
On the image processing side, non-linear adaptive filtering of the projection data has been proposed (see J. Hsieh: “Adaptive streak artifact reduction in computed tomography resulted from excessive x-ray photon noise” in “Med. Phys.” vol. 25(11), 1998, pp. 2134-2147; and M. Kachelrieb, O. Watzke, and W. A. Kalender: “Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice and cone-beam CT” in “Med. Phys.” vol. 28(4), 2001, pp. 475-490). Parameters of the filters are locally changed in dependency on the projection data. Depending on the application, the non-linear filtering may have essential disadvantages with regard to the time-consuming image processing.
Further disadvantages of the conventional techniques are given by the fact that increasing the sampling density allows an increased resolution of the image reconstruction. Thus, the artifacts are not suppressed, but rather represented with increased resolution as well. Furthermore, practical restrictions exist with regard to the image processing approaches as a result of difficulties for defining the non-linear filters, in particular in medical applications. The main problem occurs if image details of interest are suppressed together with noisy imaging artifacts. Furthermore, the image details of interest cannot be reconstructed after a local application of a non-linear filter.